Object recognition scanning systems and methods for implementing artificial based item determination

Abstract

Object recognition scanning systems and methods for implementing artificial intelligence based item determination are disclosed herein. Example object recognition scanning systems and methods include imaging, by an imager having a field of view (FOV) extending over a scanning area, one or more items within the FOV, and receiving image data of an item imaged by the imager during a scanning session. A trained object recognition model, taking the image data as input, determines a product identification probability for the item. The object recognition scanning systems and methods include executing one of: (a) a first decoding sequence including, (b) a second decoding sequence, or (c) a mismatch detection sequence.

The invention claimed is: 
     
       1. An object recognition scanning system comprising:
 an imager having a field of view (FOV) extending over a scanning area, the imager configured to image one or more items within the FOV; 
 one or more processors configured to receive image data of an imaged item imaged by the imager during a scanning session; and 
 an object recognition model stored in a memory communicatively coupled to the one or more processors, 
 the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
 determine, by the object recognition model taking the image data as input, a product identification probability for the imaged item, the product identification probability being indicative of a level of confidence that the imaged item is one of multiple items stored in the object recognition model; 
 responsive to the product identification probability meeting or exceeding a first product identification threshold value and being less than a second product identification threshold value, determine a product type based on identifying at least a portion of a barcode within the image data and transmit the product type to a host when the product type matches the one of multiple items stored in the object recognition model; and 
 responsive to the product identification probability meeting or exceeding the second product identification threshold value, determine the product type based on the one of multiple items stored in the object recognition model and transmitting the product type to the host without regard for the at least a portion of a barcode. 
 
 
     
     
       2. The object recognition scanning system of  claim 1 , further comprising a second imager having a second FOV extending over the scanning area, the second imager configured to image one or more items within the second FOV, wherein the one or more processors are configured to receive second image data from the second imager during the scanning session. 
     
     
       3. The object recognition scanning system of  claim 2 , wherein the imager and the second imager comprise a bioptic imager. 
     
     
       4. The object recognition scanning system of  claim 2 , wherein the image data imaged by the imager comprises data representing one or more images of the item, and wherein the second image data comprises data representing the at least a portion of a barcode. 
     
     
       5. The object recognition scanning system of  claim 1 , wherein the instructions cause the one or more processors to:
 responsive to the product identification probability meeting or exceeding the first product identification threshold value and being less than the second product identification threshold value, trigger a mismatch event when the product type does not match the one of multiple items stored in the object recognition model. 
 
     
     
       6. The object recognition scanning system of  claim 1 , wherein the object recognition model comprises a convolutional neural network, the convolutional neural network trained with image data representative of the item. 
     
     
       7. The object recognition scanning system of  claim 6 , wherein the convolutional neural network is further trained with image data representative of a barcode corresponding to the item. 
     
     
       8. The object recognition scanning system of  claim 1 , wherein the one or more processors are co-located with the imager in a housing of a reader. 
     
     
       9. The object recognition scanning system of  claim 1 , wherein the one or more processors are located at a server remote to the imager, wherein the server is communicatively coupled to the imager via a computer network. 
     
     
       10. An object recognition scanning system comprising:
 an imager having a field of view (FOV) extending over a scanning area, the imager configured to image one or more items within the FOV; 
 one or more processors configured to receive image data of an imaged item imaged by the imager during a scanning session; and 
 an object recognition model stored in a memory communicatively coupled to the one or more processors, 
 the memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
 determine, by the object recognition model taking the image data as input, a product identification probability for the imaged item, the product identification probability being indicative of a level of confidence that the imaged item is one of multiple items stored in the object recognition model; 
 responsive to the product identification probability meeting or exceeding a product identification threshold value and the one of multiple items stored in the object recognition model being a priority item, determine a product type based on identifying at least a portion of a barcode within the image data and transmit the product type to a host when the product type matches the one of multiple items stored in the object recognition model; and 
 responsive to the product identification probability meeting or exceeding the product identification threshold value and the one of multiple items stored in the object recognition model being a non-priority item, determining the product type based on the one of multiple items stored in the object recognition model and transmit the product type to the host without regard for the at least a portion of a barcode. 
 
 
     
     
       11. The object recognition scanning system of  claim 10 , further comprising a second imager having a second FOV extending over the scanning area, the second imager configured to image one or more items within the second FOV, wherein the one or more processors are configured to receive second image data from the second imager during the scanning session. 
     
     
       12. The object recognition scanning system of  claim 11 , wherein the imager and the second imager comprise a bioptic imager. 
     
     
       13. The object recognition scanning system of  claim 12 , wherein the image data imaged by the imager comprises data representing one or more images of the item, and wherein the second image data comprises data representing the at least a portion of a barcode. 
     
     
       14. The object recognition scanning system of  claim 10 , wherein the instructions cause the one or more processors to:
 responsive to the product identification probability meeting or exceeding the product identification threshold the one of multiple items stored in the object recognition model being the priority item, trigger a mismatch event when the product type does not match the one of multiple items stored in the object recognition model. 
 
     
     
       15. The object recognition scanning system of  claim 10 , wherein the object recognition model comprises a convolutional neural network, the convolutional neural network trained with image data representative of the item. 
     
     
       16. The object recognition scanning system of  claim 15 , wherein the convolutional neural network is further trained with image data representative of a barcode corresponding to the item. 
     
     
       17. The object recognition scanning system of  claim 10 , wherein the one or more processors are co-located with the imager in a housing of a reader. 
     
     
       18. The object recognition scanning system of  claim 10 , wherein the one or more processors are located at a server remote to the imager, wherein the server is communicatively coupled to the imager via a computer network. 
     
     
       19. A tangible machine-readable medium comprising instructions for implementing item determination that, when executed, cause a machine to at least:
 image, by an imager having a field of view (FOV) extending over a scanning area, one or more items within the FOV; 
 receive, at one or more processors, image data of an item imaged by the imager during a scanning session; 
 determine, by an object recognition model taking the image data as input, a product identification probability for the imaged item, the product identification probability being indicative of a level of confidence that the imaged item is one of multiple items stored in the object recognition model; 
 responsive to the product identification probability meeting or exceeding a first product identification threshold value and being less than a second product identification threshold value, determine a product type based on identifying at least a portion of a barcode within the image data and transmit the product type to a host when the product type matches the one of multiple items stored in the object recognition model; and 
 responsive to the product identification probability meeting or exceeding the second product identification threshold value, determining the product type based on the one of multiple items stored in the object recognition model and transmit the product type to the host without regard for the at least a portion of a barcode. 
 
     
     
       20. A method for item determination comprising:
 imaging, by an imager having a field of view (FOV) extending over a scanning area, one or more items within the FOV; 
 receiving, at one or more processors, image data of an item imaged by the imager during a scanning session; 
 determining, by the object recognition model taking the image data as input, a product identification probability for the imaged item, the product identification probability being indicative of a level of confidence that the imaged item is one of multiple items stored in the object recognition model; 
 responsive to the product identification probability meeting or exceeding a product identification threshold value and the one of multiple items stored in the object recognition model being a priority item, determining a product type based on identifying at least a portion of a barcode within the image data and transmitting the product type to a host when the product type matches the one of multiple items stored in the object recognition model; and 
 responsive to the product identification probability meeting or exceeding the product identification threshold value and the one of multiple items stored in the object recognition model being a non-priority item, determining the product type based on the one of multiple items stored in the object recognition model and transmitting the product type to the host without regard for the at least a portion of a barcode.